Closing the AI readiness gap

Aglobal CIMA survey shows that 88% of finance leaders view AI as a gamechanger, yet only 8% feel their organisation is very well prepared to manage it. This has created a clear “readiness gap”, where awareness is high but confidence and practical capability to leverage AI remain low – providing a critical opportunity for finance leaders to lead the AI transformation.

During Productivity Week (27 April-1 May 2026), CIMA hosted a panel discussion, Future-Ready Finance, moderated by Professor Bart van Ark, Managing Director of The Productivity Institute. He was joined by leading voices from academia, government and business, including Tera Allas CBE, Senior Advisor, Honorary Professor and Chair, Alexander Ilkun, ACMA, CGMA, Treasury Practitioner and Consultant, and Fred Fowler, FCMA, CGMA, Global Head of Finance Services at Coty Inc.

The panel explored how organisations can use AI to boost productivity, redesign roles around higher-value work, and equip finance teams with the skills needed to thrive in a rapidly evolving environment. Here is a summary of that discussion and insights.

But these results represent outcomes, not the underlying explanation. The real driver is what our firms are doing within their own markets: strengthening capability, developing new service offerings, and responding directly to the needs of clients navigating increasingly complex environments.

Many finance teams have achieved isolated efficiency gains by automating specific tasks. However, the bigger challenge lies in scaling those gains across the enterprise. Panellists noted that moving beyond pilots requires redesigning end-to-end processes, so gains in individual tasks add up to overall performance improvements.

While bottom-up experimentation with AI by engaged employees is valuable, it must be coupled with deliberate top-down integration and governance to prevent fragmentation and maximise impact. Without this, organisations risk fragmentation and missed opportunities. The goal is to identify high-volume, routine tasks suitable for full automation (with humans overseeing design and exceptions), and reshaping workflows to redirect capacity towards higher-value analysis and decision support.

Crucially, incremental innovations should drive visible gains in efficiency, quality and profitability, not just localised time savings.

Realising AI’s productivity promise requires a leadership approach that is both enabling and disciplined. Finance leaders must encourage innovation while setting clear guardrails for the responsible use of AI. This includes establishing policies for data quality and security and protecting sensitive information while, creating a “safe-to-fail” culture that supports learning and collaboration. Positioning AI as a tool to augment human work, rather than a cost‑cutting exercise, will support workforce buy‑in and engagement with change.

Equally important is how success is measured. Traditional metrics such as reduced manual effort or lower transaction costs remain relevant, but they are no longer sufficient on their own. Broader indicators, including adoption and usage rates of AI tools, faster reporting cycles, improved decision quality and reduced errors, provide a more complete picture of impact.

Some benefits, such as new insights or services, are difficult to quantify. As a result, finance leaders are moving towards multi-dimensional metrics that connect AI-driven efficiencies with profitability and strategic goals.

The biggest barriers to AI‑driven productivity gains are people‑related rather than technical. Skills gaps, workforce readiness and process design remain the most significant challenges. Our survey shows that 41% of finance leaders cite a lack of skills as the primary barrier to productivity, while 37% point to low employee engagement. Structural issues such as incompatible IT systems and poor coordination exist but rank lower by comparison.

This underlines a critical truth; technology alone cannot deliver lasting productivity gains. Without the right talent, motivation and ways of working, AI investments struggle to scale beyond isolated use cases.

For finance leaders, this means taking an active role in orchestrating transformation, not just sponsoring technology programmes. Many are already advancing digital finance agendas through automation, advanced analytics and more agile planning models. The next challenge is ensuring these initiatives are coordinated, scaled and embedded across the organisation.

Effective finance leaders focus on building internal capability, identifying AI champions, and systematically expanding successful pilots. Clear governance frameworks, combined with the freedom for teams to experiment within defined boundaries, help accelerate adoption while avoiding fragmentation.

The greatest risk today is not widespread job losses but failing to use AI to redesign human-centred processes and unlock productivity gains. Closing the AI readiness gap, particularly around skills, engagement and process design, is critical to translating isolated gains into sustained organisational performance.

The opportunity for finance leaders is to place themselves at the centre of the transformation, leading capability-building, governance and cultural change. Those who combine technological fluency with people‑centred change leadership will be best placed to embed AI responsibly and at scale across their organisations.